Identifying priority and bright spot areas for improving diabetes care: a geospatial approach

The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran’s I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community.

A 4-Yr Survey of the Range of Ticks and Tick-Borne Pathogens in the Lehigh Valley Region of Eastern Pennsylvania

Questing ticks were surveyed by dragging in forested habitats within the Lehigh Valley region of eastern Pennsylvania for four consecutive summers (2015–2018). A high level of inter-annual variation was found in the density of blacklegged tick nymphs, Ixodes scapularis Say, with a high density of host-seeking nymphs (DON) in summer 2015 and 2017 and a relatively low DON in summer 2016 and 2018. Very few American dog ticks (Dermacentor variabilis Say) and Ixodes cookei Packard were collected. Lone star ticks (Amblyomma americanum L.) and longhorned ticks (Haemaphysalis longicornis Neumann) were not represented among the 6,398 ticks collected. For tick-borne pathogen surveillance, DNA samples from 1,721 I. scapularis nymphs were prepared from specimens collected in summers 2015–2017 and screened using qPCR, high resolution melting analysis, and DNA sequencing when necessary. The overall 3-yr nymphal infection prevalence of Borrelia burgdorferi was 24.8%, Borrelia miyamotoi was 0.3%, Anaplasma phagocytophilum variant-ha was 0.8%, and Babesia microti was 2.8%. Prevalence of coinfection with B. burgdorferi and B. microti as well as B. burgdorferi and A. phagocytophilum variant-ha were significantly higher than would be expected by independent infection. B. burgdorferi nymphal infection prevalence is similar to what other studies have found in the Hudson Valley region of New York, but levels of B. microti and A. phagocytophilum variant-ha nymphal infection prevalence are relatively lower. This study reinforces the urgent need for continued tick and pathogen surveillance in the Lehigh Valley region.